A Review of Deep Learning Methods Applied on Load Forecasting

The utility industry has invested widely in smart grid (SG) over the past decade. They considered it the future electrical grid while the information and electricity are delivered in two-way flow. SG has many Artificial Intelligence (AI) applications such as Artificial Neural Network (ANN), Machine Learning (ML) and Deep Learning (DL). Recently, DL has been a hot topic for AI applications in many fields such as time series load forecasting. This paper introduces the common algorithms of DL in the literature applied to load forecasting problems in the SG and power systems. The intention of this survey is to explore the different applications of DL that are used in the power systems and smart grid load forecasting. In addition, it compares the accuracy results RMSE and MAE for the reviewed applications and shows the use of convolutional neural network CNN with k-means algorithm had a great percentage of reduction in terms of RMSE.

[1]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[2]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[3]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[4]  Ming-Wei Chang,et al.  Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.

[5]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[6]  Maarouf Saad,et al.  An efficient approach for short term load forecasting using artificial neural networks , 2006 .

[7]  Mohsen Hayati,et al.  Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region , 2007 .

[8]  Zhihui Zhu,et al.  Hybrid of EMD and SVMs for Short-Term Load Forecasting , 2007, 2007 IEEE International Conference on Control and Automation.

[9]  M. Tarafdar Haque,et al.  Application of Neural Networks in Power Systems; A Review , 2007 .

[10]  Le Zhang,et al.  Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

[11]  C. L. Philip Chen,et al.  Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting , 2015, IEEE Transactions on Sustainable Energy.

[12]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[13]  Hongseok Kim,et al.  Deep neural network based demand side short term load forecasting , 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[14]  Daniel L. Marino,et al.  Building energy load forecasting using Deep Neural Networks , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[15]  Bernhard Sick,et al.  Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[16]  Zijun Zhang,et al.  Short-Term Electricity Price Forecasting With Stacked Denoising Autoencoders , 2017, IEEE Transactions on Power Systems.

[17]  Lei Huang,et al.  Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach , 2017, 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).

[18]  David J. Hill,et al.  Short-Term Residential Load Forecasting Based on Resident Behaviour Learning , 2018, IEEE Transactions on Power Systems.

[19]  Ran Li,et al.  Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN , 2018, IEEE Transactions on Smart Grid.